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Load VATComply.com data to DuckDB

Build a VATComply.com to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the VATComply.com API base URL, auth, endpoints, and incremental loading.

SourceVATComply.comVATComply.com API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

VATcomply is a free API service that provides VAT number validation, foreign exchange rates, IBAN validation, and IP geolocation tools. Everything needed to build a working VATComply.com → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.


Build your VATComply.com to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from VATComply.com to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the VATComply.com API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.


VATComply.com API at a glance

Base URLhttps://api.vatcomply.com
Example endpointGET rates
Authenticationno authentication required — sent in the request header
PaginationNot paginated
API referencehttps://api.vatcomply.com/docs

These values come from the VATComply.com API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the VATComply.com API?

The VATcomply API does not require any authentication or API keys for direct access.

No credentials required. The VATComply.com API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.


What VATComply.com data can I load into DuckDB?

These are the VATComply.com endpoints dlt can load into DuckDB:

| Resource | Endpoint | Method | Data selector | Description |\n| :--- | :--- | :--- | :--- | :--- |\n| countries | /countries | GET | | Get list of countries |\n| currencies | /currencies | GET | | Get list of supported currencies |\n| geolocate | /geolocate | GET | | Geolocate by IP |\n| iban | /iban | GET | | Validate IBAN |\n| vat | /vat | GET | | Validate VAT number |\n| vat_rates | /vat_rates | GET | | Get VAT rates |\n| rates | /rates | GET | | Get exchange rates | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |


How do I load only new VATComply.com records?

The VATComply.com API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "rates", "endpoint": { "path": "rates", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.


What does the generated VATComply.com pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading vat and rates from the VATComply.com API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def vatcomply_com_source(): config: RESTAPIConfig = { "client": { "base_url": "https://api.vatcomply.com", }, "resources": [ {"name": "rates", "endpoint": {"path": "rates"}}, {"name": "vat_rates", "endpoint": {"path": "vat_rates"}} ], } yield from rest_api_resources(config) def load_vatcomply_com_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="vatcomply_com_pipeline", destination="duckdb", dataset_name="vatcomply_com_data", ) load_info = pipeline.run(vatcomply_com_source()) print(load_info) if __name__ == "__main__": load_vatcomply_com_to_duckdb()

Run it with python vatcomply_com_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query VATComply.com data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

import dlt data = dlt.pipeline("vatcomply_com_pipeline").dataset() df = data.rates.df() print(df.head())

SQL:

SELECT * FROM vatcomply_com_data.rates LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.


How do I deploy the VATComply.com to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

  • Deploy & schedule — run the pipeline as a managed job with automatic retries.
  • Monitor — observable job queues, alerting, and load metrics for every run.
  • Transform — promote raw VATComply.com loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load VATComply.com data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.


Next steps

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